FldtMatch: Improving Unbalanced Data Classification via Deep Semi-Supervised Learning with Self-Adaptive Dynamic Threshold

Xin Wu, Jingjing Xu, Kuan Li, Jianping Yin and Jian Xiong ()
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Xin Wu: Department of Artificial Intelligence and Data Science, Guangzhou Xinhua University, 248 Yanjiangxi Road, Machong Town, Dongguan 523133, China
Jingjing Xu: Department of Artificial Intelligence and Data Science, Guangzhou Xinhua University, 248 Yanjiangxi Road, Machong Town, Dongguan 523133, China
Kuan Li: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Jianping Yin: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Jian Xiong: Department of Artificial Intelligence and Data Science, Guangzhou Xinhua University, 248 Yanjiangxi Road, Machong Town, Dongguan 523133, China

Mathematics, 2025, vol. 13, issue 3, 1-21

Abstract: Among the many methods of deep semi-supervised learning (DSSL), the holistic method combines ideas from other methods, such as consistency regularization and pseudo-labeling, with great success. This method typically introduces a threshold to utilize unlabeled data. If the highest predictive value from unlabeled data exceeds the threshold, the associated class is designated as the data’s pseudo-label. However, current methods utilize fixed or dynamic thresholds, disregarding the varying learning difficulties across categories in unbalanced datasets. To overcome these issues, in this paper, we first designed Cumulative Effective Labeling (CEL) to reflect a particular class’s learning difficulty. This approach differs from previous methods because it uses effective pseudo-labels and ground truth, collectively influencing the model’s capacity to acquire category knowledge. In addition, based on CEL, we propose a simple but effective way to compute the threshold, Self-adaptive Dynamic Threshold (SDT). It requires a single hyperparameter to adjust to various scenarios, eliminating the necessity for a unique threshold modification approach for each case. SDT utilizes a clever mapping function that can solve the problem of differential learning difficulty of various categories in an unbalanced image dataset that adversely affects dynamic thresholding. Finally, we propose a deep semi-supervised method with SDT called FldtMatch. Through theoretical analysis and extensive experiments, we have fully proven that FldtMatch can overcome the negative impact of unbalanced data. Regardless of the choice of the backbone network, our method achieves the best results on multiple datasets. The maximum improvement of the macro F1-Score metric is about 5.6% in DFUC2021 and 2.2% in ISIC2018.

Keywords: deep semi-supervised learning; unbalanced data; classification; dynamic threshold (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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